scholarly journals Classification Models for Early Detection of Prostate Cancer

2008 ◽  
Vol 2008 ◽  
pp. 1-7 ◽  
Author(s):  
Joerg D. Wichard ◽  
Henning Cammann ◽  
Carsten Stephan ◽  
Thomas Tolxdorff
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Cross Validation ◽  
Early Stage ◽  
Clinical Work ◽  
Classification Performance ◽  
Classification Models ◽  
Validation Procedure

We investigate the performance of different classification models and their ability to recognize prostate cancer in an early stage. We build ensembles of classification models in order to increase the classification performance. We measure the performance of our models in an extensive cross-validation procedure and compare different classification models. The datasets come from clinical examinations and some of the classification models are already in use to support the urologists in their clinical work.

scholarly journals Automated Assay of a Four-Protein Biomarker Panel for Improved Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 325
Author(s):  
Christopher Walker ◽  
Tuan-Minh Nguyen ◽  
Shlomit Jessel ◽  
Ayesha B. Alvero ◽  
Dan-Arin Silasi ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Detection ◽  
Predictive Value ◽  
Early Stage ◽  
Ca 125 ◽  
Healthy Controls ◽  
Classification Models ◽  
Serum Samples ◽  
Protein Biomarker ◽  
Biomarker Panel

Background: Mortality from ovarian cancer remains high due to the lack of methods for early detection. The difficulty lies in the low prevalence of the disease necessitating a significantly high specificity and positive-predictive value (PPV) to avoid unneeded and invasive intervention. Currently, cancer antigen- 125 (CA-125) is the most commonly used biomarker for the early detection of ovarian cancer. In this study we determine the value of combining macrophage migration inhibitory factor (MIF), osteopontin (OPN), and prolactin (PROL) with CA-125 in the detection of ovarian cancer serum samples from healthy controls. Materials and Methods: A total of 432 serum samples were included in this study. 153 samples were from ovarian cancer patients and 279 samples were from age-matched healthy controls. The four proteins were quantified using a fully automated, multi-analyte immunoassay. The serum samples were divided into training and testing datasets and analyzed using four classification models to calculate accuracy, sensitivity, specificity, PPV, negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). Results: The four-protein biomarker panel yielded an average accuracy of 91% compared to 85% using CA-125 alone across four classification models (p = 3.224 × 10−9). Further, in our cohort, the four-protein biomarker panel demonstrated a higher sensitivity (median of 76%), specificity (median of 98%), PPV (median of 91.5%), and NPV (median of 92%), compared to CA-125 alone. The performance of the four-protein biomarker remained better than CA-125 alone even in experiments comparing early stage (Stage I and Stage II) ovarian cancer to healthy controls. Conclusions: Combining MIF, OPN, PROL, and CA-125 can better differentiate ovarian cancer from healthy controls compared to CA-125 alone.

scholarly journals On data collection time by an electronic nose

2021 ◽  
Vol 11 (6) ◽  
pp. 4767
Author(s):  
Piotr Borowik ◽  
Leszek Adamowicz ◽  
Rafał Tarakowski ◽  
Krzysztof Siwek ◽  
Tomasz Grzywacz
Keyword(s):  
Electronic Nose ◽  
Cross Validation ◽  
Model Performance ◽  
Classification Performance ◽  
Optimal Time ◽  
Classification Models ◽  
Machine Learning Classification ◽  
Average Accuracy ◽  
Complex Features ◽  
Collection Time

<p>We use electronic nose data of odor measurements to build machine learning classification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation.</p>

scholarly journals A Rich Array of Prostate Cancer Molecular Biomarkers: Opportunities and Challenges

2019 ◽  
Vol 20 (8) ◽  
pp. 1813 ◽  
Author(s):  
Indu Kohaar ◽  
Gyorgy Petrovics ◽  
Shiv Srivastava
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Biomarker Discovery ◽  
Early Stage ◽  
Specific Antigen ◽  
Cancer Type ◽  
Genomic Technologies ◽  
Individualized Approach ◽  
Prognostic Tests ◽  
Disease Stratification

Prostate cancer is the most prevalent non-skin cancer in men and is the leading cause of cancer-related death. Early detection of prostate cancer is largely determined by a widely used prostate specific antigen (PSA) blood test and biopsy is performed for definitive diagnosis. Prostate cancer is asymptomatic in the early stage of the disease, comprises of diverse clinico-pathologic and progression features, and is characterized by a large subset of the indolent cancer type. Therefore, it is critical to develop an individualized approach for early detection, disease stratification (indolent vs. aggressive), and prediction of treatment response for prostate cancer. There has been remarkable progress in prostate cancer biomarker discovery, largely through advancements in genomic technologies. A rich array of prostate cancer diagnostic and prognostic tests has emerged for serum (4K, phi), urine (Progensa, T2-ERG, ExoDx, SelectMDx), and tumor tissue (ConfirmMDx, Prolaris, Oncoytype DX, Decipher). The development of these assays has created new opportunities for improving prostate cancer diagnosis, prognosis, and treatment decisions. While opening exciting opportunities, these developments also pose unique challenges in terms of selecting and incorporating these assays into the continuum of prostate cancer patient care.

Nano-Based PSA Biosensors: An Early Detection Technique of Prostate Cancer

2014 ◽  
Vol 20 ◽  
pp. 87-98 ◽  
Author(s):  
Mansoor Ani Najeeb ◽  
Sankaranarayana Pillai ◽  
Murthy Chavali
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Prostate Specific Antigen ◽  
Normal Level ◽  
Early Stage ◽  
Prostate Gland ◽  
Specific Antigen ◽  
Antigen Test ◽  
Review Journal ◽  
Prostate Specific Antigen Test

Prostate-specific antigen or PSA is a protein biomarker which is produced by the cells of prostate gland. The normal level of PSA in blood is often elevated in men with prostate cancer. In India, prostate cancer is one among the five, mostly cited cancer in men and it is getting increased by 1% every year. The screening test used for prostate cancer is the Prostate Specific Antigen test. The first PSA assay was determined in 1979. Most of the current techniques used for PSA detection are utilizing large analyzers, there by increased time and cost. Increased PSA levels can also because of prostatitis (inflammation of the prostate gland) or due to many other reasons. A proper technique to differential diagnose this disease is also an issue. The benchmark for the PSA level cannot be determined accurately. For this, various types of biosensors are used. This review journal is is trying to analyze variouus Nano-Biosensors used for early detection of PSA from blood in an early stage itself.

1596: Choices for Definitive Treatment of Early-Stage Prostate Cancer: Results from the Compare Registry

2006 ◽  
Vol 175 (4S) ◽  
pp. 514-514
Author(s):  
David G. McLeod ◽  
Oliver Sartor ◽  
Paul F. Schellhammer ◽  
Anthony V. D'Amico ◽  
Susan Halabi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Early Stage ◽  
Definitive Treatment

1820: Complexed PSA for Early Detection of Prostate Cancer: A Multi Institutional Study

2004 ◽  
Vol 171 (4S) ◽  
pp. 481-481
Author(s):  
Ravery V. Vincent ◽  
Chautard D. Denis ◽  
Arnauld A. Villers ◽  
Laurent Boccon Gibbod
Keyword(s):  
Prostate Cancer ◽  
Early Detection

1068: TP53 Gene Mutations as a Prognostic Marker for PSA Progression in Early Stage Prostate Cancer

2004 ◽  
Vol 171 (4S) ◽  
pp. 282-282
Author(s):  
Markus D. Sachs ◽  
Horst Schlechte ◽  
Katrin Schiemenz ◽  
Severin V. Lenk ◽  
Dietmar Schnorr ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Prognostic Marker ◽  
Early Stage ◽  
Gene Mutations ◽  
Tp53 Gene ◽  
Psa Progression ◽  
Tp53 Gene Mutations
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